---
license: mit
language:
- grc
- la
- sv
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- bge-m3
- cross-lingual
- classical-philology
- intertextuality
- citation-detection
base_model: BAAI/bge-m3
datasets:
- Ericu950/classical-swedish-citations
- Ericu950/classical-swedish-synthetic-parallel
---

# intertext-classical-swedish-window

A cross-lingual bi-encoder for finding classical Greek and Latin citations in Swedish prose, operating on **5-sentence windows** rather than single sentences. The wider context lets the model match citations that are paraphrased, expanded, or spread across several Swedish sentences — cases where surface form barely overlaps but meaning does.

For sentence-level matching, see the companion model
**[Ericu950/intertext-classical-swedish-sentence](https://huggingface.co/Ericu950/intertext-classical-swedish-sentence)**.

## Quick start

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Ericu950/intertext-classical-swedish-window")
model.max_seq_length = 320

src = (
    "Γινώσκετε ἄρα ὅτι οἱ ἐκ πίστεως, οὗτοι υἱοί εἰσιν Ἀβραάμ. "
    "Προϊδοῦσα δὲ ἡ γραφὴ ὅτι ἐκ πίστεως δικαιοῖ τὰ ἔθνη ὁ θεός, "
    "προευηγγελίσατο τῷ Ἀβραὰμ ὅτι ἐνευλογηθήσονται ἐν σοὶ πάντα τὰ ἔθνη."
)
candidates = [
    "Veten därför, att de som äro av tron, de äro Abrahams barn. "
    "Och eftersom Skriften förutsåg att Gud genom tron rättfärdigar hedningarna, "
    "förkunnade hon i förväg för Abraham detta glada budskap...",

    "Han gick genom rummet och stannade vid fönstret. "
    "Han såg ut över taken och funderade på vad som hade hänt. "
    "Klockan på torget slog tre. Han vände sig om...",
]

embs = model.encode([src] + candidates, normalize_embeddings=True)
scores = embs[0] @ embs[1:].T
for c, s in zip(candidates, scores):
    print(f"{s:+.3f}  {c[:80]}...")
```

## Intended use

The model is the passage-level retrieval head of a pipeline for discovering classical citations in Swedish literary corpora. Typical use:

1. Encode classical (Greek/Latin) source windows and Swedish corpus windows with this model.
2. Run dense retrieval (cosine) to surface candidate citation pairs.
3. Rerank with a cross-encoder and apply additional features (rarity, sentence-level agreement, contextual support).
4. Filter survivors with an LLM judge.

The model also functions as a general Greek/Latin/Swedish passage encoder, but it's specifically optimized for citation detection at window granularity (~5 sentences).

## Training data

Training data comes from
[Ericu950/classical-swedish-citations](https://huggingface.co/datasets/Ericu950/classical-swedish-citations) (windows config). A "window" in this dataset is a 5-sentence chunk centered on a target sentence; for sentences near a work's boundary, the window is truncated accordingly.


## Evaluation

Held-out set: 367 unique source anchors (windows) from `Ericu950/classical-swedish-citations`, split off before mining (no leakage). The document pool contains:

- 367 gold target Swedish windows
- ~39,500 real production false-positive Swedish windows (labeled negative pairs from the same dataset, training-side)
- ~5,000 random Swedish windows sampled from a 4M-window corpus

Total document pool: ~45,000 docs per query.

| Metric | v2 base | v3 (this model) | Δ |
|---|---|---|---|
| nDCG@10 | 0.839 | **0.853** | +0.014 |
| Accuracy@1 | 63.2% | **65.4%** | +2.2% |
| Accuracy@5 | 99.7% | 100.0% | +0.3% |
| Accuracy@10 | 99.7% | 100.0% | +0.3% |
| Accuracy@25 | 100.0% | 100.0% | — |

Window retrieval is intrinsically harder than sentence retrieval — longer text means more surface overlap with distractors. The fine-tune produces a meaningful improvement at the top of the ranking (the only place there's room): gold is now always found by rank 10, and the top-1 hit rate improves by 2.2 absolute percentage points.

## Limitations

- **Domain:** trained primarily on biblical, philosophical, and literary citations. Performance on other domains is unknown.
- **Granularity:** optimized for 5-sentence windows. For tight single-line citations, the sentence-level companion model may be sharper.
- **Edge windows:** sentences near a work's start or end have shorter windows (1–4 sentences). The model sees these but performance on them may differ from full 5-sentence windows.
- **Language coverage:** Greek, Latin, and Swedish only. The base BGE-M3 is multilingual, but this fine-tune may have shifted geometry away from other languages.
- **Citations vs. translations:** the model conflates citation, translation, and close paraphrase. It cannot distinguish between "this passage is quoting Plato" and "this passage independently translates Plato."
- **Sequence length:** max_seq_length is 320 tokens. Very long Swedish sentences (or windows packed with long compounds) may be truncated.

## Related artifacts

- **Sentence-level model:** [Ericu950/intertext-classical-swedish-sentence](https://huggingface.co/Ericu950/intertext-classical-swedish-sentence)
- **Labeled citation data:** [Ericu950/classical-swedish-citations](https://huggingface.co/datasets/Ericu950/classical-swedish-citations)
- **Synthetic parallel data:** [Ericu950/classical-swedish-synthetic-parallel](https://huggingface.co/datasets/Ericu950/classical-swedish-synthetic-parallel)
- **Source corpus:** [Ericu950/classical-swedish-corpus](https://huggingface.co/datasets/Ericu950/classical-swedish-corpus)

## Citation

```bibtex
@misc{intertext_classical_swedish_window_2026,
  author       = {Cullhed, Eric},
  title        = {intertext-classical-swedish-window: a window-level bi-encoder for cross-lingual classical citation detection},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Ericu950/intertext-classical-swedish-window}},
}
```